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Slow Feature Analysis as Variational Inference Objective

31 May 2025
Merlin Schuler
Laurenz Wiskott
ArXiv (abs)PDFHTML
Main:8 Pages
4 Figures
Bibliography:2 Pages
Appendix:3 Pages
Abstract

This work presents a novel probabilistic interpretation of Slow Feature Analysis (SFA) through the lens of variational inference. Unlike prior formulations that recover linear SFA from Gaussian state-space models with linear emissions, this approach relaxes the key constraint of linearity. While it does not lead to full equivalence to non-linear SFA, it recasts the classical slowness objective in a variational framework. Specifically, it allows the slowness objective to be interpreted as a regularizer to a reconstruction loss. Furthermore, we provide arguments, why -- from the perspective of slowness optimization -- the reconstruction loss takes on the role of the constraints that ensure informativeness in SFA. We conclude with a discussion of potential new research directions.

View on arXiv
@article{schüler2025_2506.00580,
  title={ Slow Feature Analysis as Variational Inference Objective },
  author={ Merlin Schüler and Laurenz Wiskott },
  journal={arXiv preprint arXiv:2506.00580},
  year={ 2025 }
}
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